Designing Energy-Efficient Buildings in Urban Centers through Machine Learning and Enhanced Clean Water Management.

Rainwater Harvesting (RWH) is increasingly recognized as a vital sustainable practice in urban environments, aimed at enhancing water conservation and reducing energy consumption. This study introduces an innovative integration of nano-composite materials as Silver Nanoparticles (AgNPs) into RWH systems to elevate water treatment efficiency and assess the resulting environmental and energy-saving benefits. Utilizing a regression analysis approach with Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), this study will reach the study objective. In this study, the inputs are building attributes, environmental parameters, sociodemographic factors, and the algorithms SVM and KNN. At the same time, the outputs are predicted energy consumption, visual comfort outcomes, ROC-AUC values, and Kappa Indices. The integration of AgNPs into RWH systems demonstrated substantial environmental and operational benefits, achieving a 57% reduction in microbial content and 20% reductions in both chemical usage and energy consumption. These improvements highlight the potential of AgNPs to enhance water safety and reduce the environmental impact of traditional water treatments, making them a viable alternative for sustainable water management. Additionally, the use of a hybrid SVM-KNN model effectively predicted building energy usage and visual comfort, with high accuracy and precision, underscoring its utility in optimizing urban building environments for sustainability and comfort.

A review on advancements in atmospheric microplastics research: The pivotal role of machine learning.

Microplastics (MPs), recognized as emerging pollutants, pose significant potential impacts on the environment and human health. The investigation into atmospheric MPs is nascent due to the absence of effective characterization methods, leaving their concentration, distribution, sources, and impacts on human health largely undefined with evidence still emerging. This review compiles the latest literature on the sources, distribution, environmental behaviors, and toxicological effects of atmospheric MPs. It delves into the methodologies for source identification, distribution patterns, and the contemporary approaches to assess the toxicological effects of atmospheric MPs. Significantly, this review emphasizes the role of Machine Learning (ML) and Artificial Intelligence (AI) technologies as novel and promising tools in enhancing the precision and depth of research into atmospheric MPs, including but not limited to the spatiotemporal dynamics, source apportionment, and potential health impacts of atmospheric MPs. The integration of these advanced technologies facilitates a more nuanced understanding of MPs' behavior and effects, marking a pivotal advancement in the field. This review aims to deliver an in-depth view of atmospheric MPs, enhancing knowledge and awareness of their environmental and human health impacts. It calls upon scholars to focus on the research of atmospheric MPs based on new technologies of ML and AI, improving the database as well as offering fresh perspectives on this critical issue.

An evaluation of air quality impact prediction performance undertaken as part of environmental impact assessment (EIA) in India.

Effective implementation of the Environmental Impact Assessment (EIA) is recognised as a global issue, in particular the impact prediction stage, which is the 'core' of EIA. Consisting of four stages: impact identification, impact assessment, significance evaluation, and mitigation measures on the possible environmental repercussions of project developmental activities, the efficacy of impact prediction can define the quality of the EIA process, which will better align environmental decision-making to sustainable development. The weakness of impact prediction in EIA demands more study to enhance practice. Although this is widely explored in the context of developed countries such as the UK, it is particularly concerning in India. A specialised review package built from several sources is utilised to assess the efficacy of air quality impact prediction, based on Lee & Colley (1991). 20 EIA reports of Category A (mega-scale projects causing significant environmental impacts) are reviewed. This study's evaluation indicates that significance evaluation and mitigation actions are the weakest phases and a major concern while assessing air quality studies conducted as a part of EIA. Recommendations to improve the process include prioritising the cumulative impact assessment within the regulatory framework, enhancing capacity building, embedding public participation and instilling accountability among stakeholders, which can be adopted globally. Additional recommendations specifically for India are revising the National Ambient Air Quality Standards (NAAQS), restructuring the EIA review mechanism by EAC and improving mitigation measures by adopting GIS and remote sensing technologies.

Gas engine CCHP system optimization: An energy, exergy, economic, and environment analysis and optimization based on developed northern goshawk optimization algorithm.

This paper aims to enhance the design and operation of a Combined Cooling, Heating, and Power (CCHP) system utilizing a gas engine as the primary energy source for a residential building in China. An Energy, Exergy, Economic, and Environment (4E) analysis is employed to assess the system's performance and impact based on energy, exergy, economic, and environmental criteria. The effectiveness of the DNGO algorithm is evaluated on a case study site and compared with Northern Goshawk Optimization (NGO) and Genetic Algorithm (GA). The findings demonstrate that the DNGO algorithm identifies the optimal gas engine size of 130 kW. The algorithm's search capabilities are greatly enhanced by this unique blend, surpassing what traditional methods can offer. The DNGO algorithm brings several advantages, including unparalleled energy efficiency, reduced exergy destruction, and a substantial decrease in C O 2 emissions. This not only supports environmental sustainability but also aligns with global standards. Economically, the algorithm enhances the performance of the CCHP system, evident through a reduced payback period and increased annual profit. Additionally, the algorithm's rapid convergence rate allows it to reach the optimal solution faster than its counterparts, making it advantageous for time-sensitive applications. Incorporating innovative methods like chaos theory, the DNGO algorithm effectively avoids local optima, enabling a broader search for the best solution. The utilization of Lévy flight further enhances the algorithm's ability to escape local optima and navigate the search space more efficiently. Additionally, swarm intelligence is employed to simulate the collective behavior of decentralized systems, aiding in problem-solving. This research represents a significant advancement in optimization techniques for CCHP systems and offers a fresh perspective to the field of swarm-based optimization algorithms.

Health technology assessment and healthcare environmental sustainability: Prioritizing effort and maximizing impact.

The growing global focus on and sense of urgency toward improving healthcare environmental sustainability and moving to low-carbon and resilient healthcare systems is increasingly mirrored in discussions of the role of health technology assessment (HTA). This Perspective considers how HTA can most effectively contribute to these goals and where other policy tools may be more effective in driving sustainability, especially given the highly limited pool of resources available to conduct environmental assessments within HTA. It suggests that HTA might most productively focus on assessing those technologies that have intrinsic characteristics which may cause specific environmental harms or vulnerabilities, while the generic environmental impacts of most other products may be better addressed through other policy and regulatory mechanisms.

Integrated survey methodologies provide process-driven framework for marine renewable energy environmental impact assessment.

Environmental interactions of marine renewable energy developments vary from fine-scale direct (e.g. potential collision) to indirect wide-scale hydrodynamic changes altering oceanographic features. Current UK Environmental Impact Assessment (EIA) and associated Habitats Regulations Appraisal (HRA) guidelines have limited focus on underlying processes affecting distribution and movements (hence vulnerability) of top predators. This study integrates multi-trophic ship survey (active acoustics and observer data) with an upward-facing seabed platform and 3-dimensional hydrodynamic model as a process-driven framework to investigate predator-prey linkages between seabirds and fish schools. Observer-only data highlighted the need to measure physical drivers of variance in species abundances and distributions. Active acoustics indicated that in situ (preferable to modelled) data were needed to identify temporal changes in hydrodynamics to predict prey and consequently top predator presence. Revising methods to identify key habitats and environmental covariates within current regulatory frameworks will enable more robust and transferable EIA and HRA processes and outputs, and at larger scales for cumulative and strategic-level assessments, enabling future modelling of ecosystem impacts from both climate change and renewable energy extraction.

Development of web-based software for environmental impact assessment focusing on road traffic noise.

In the Republic of Korea, Environmental Impact Assessment (EIAs) precedes development projects to predict and analyze potential environmental effects. Generally, EIA noise evaluations utilize 2D noise prediction equations and correction coefficients. This method, however, offers only a sectional noise evaluation and has limitations in complex environments with diverse noise sources. Moreover, the determination of various variables during the EIA process based on subjective human judgment raises concerns about the reliability of the results. Thus, this study aims to develop software accessible via a web environment for user-friendly EIA noise evaluations. This software supports integrated data management and generates a 3D noise prediction model for more precise and realistic analysis of noise impacts, specifically focusing on road-traffic noise at this stage of development. The 3D noise prediction model and noise map generated by the developed software have been validated against through comparison with the results of onsite noise measurements and commercial EIA software, SoundPLAN. This validation aimed to assess the practical utility of the application.

Assessing the potential of nematode metabarcoding for benthic monitoring of offshore oil platforms.

Environmental DNA metabarcoding is gaining momentum as a time and cost-effective tool for biomonitoring and environmental impact assessment. Yet, its use as a replacement for the conventional marine benthic monitoring based on morphological analysis of macrofauna is still challenging. Here we propose to study the meiofauna, which is much better represented in sediment DNA samples. We focus on nematodes, which are the most numerous and diverse group of meiofauna. Our aim is to assess the potential of nematode metabarcoding to monitor impacts associated with offshore oil platform activities. To achieve this goal, we used nematode-optimized marker (18S V1V2-Nema) and universal eukaryotic marker (18S V9) region to analyse 252 sediment DNA samples collected near three offshore oil platforms in the North Sea. For both markers, we analysed changes in alpha and beta diversity in relation to distance from the platforms and environmental variables. We also defined three impact classes based on selected environmental variables that are associated with oil extraction activities and used random forest classifiers to compare the predictive performance of both datasets. Our results show that alpha- and beta-diversity of nematodes varies with the increasing distance from the platforms. The variables directly related to platform activity, such as Ba and THC, strongly influence the nematode community. The nematode metabarcoding data provide more robust predictive models than eukaryotic data. Furthermore, the nematode community appears more stable in time and space, as illustrated by the overlap of nematode datasets obtained from the same platform three years apart. A significative negative correlation between distance and Shannon diversity also advocates for higher performance of the V1V2-Nema over the V9. Overall, these results suggest that the sensitivity of nematodes is higher compared to the eukaryotic community. Hence, nematode metabarcoding has the potential to become an effective tool for benthic monitoring in marine environment.

Practicability and environmental impact assessment of synthetic fibre reinforced polymer (SFRP) stirrups in reinforced concrete beams.

In this study, the objective is to explore the practicability of incorporating synthetic fibre reinforced polymer (SFRP) stirrups into reinforced concrete beams. This investigation revolves around evaluating their effectiveness from two key perspectives: their structural performance and environmental impact. To accomplish this, four set of specimens were prepared, each integrating SFRP stirrups, and testing them under a rigorous three-point bending load test. The structural performance analysis entails a comprehensive examination on the critical design factors such as: the load-deflection relationship and the contribution these SFRP stirrups to improve the ductility performance, flexural stiffness, deformability factor, flexural toughness and energy absorption capacity. The findings of this study indicate that the SFRP stirrups exhibit commendable shear capacity, meeting the necessary requirements, and simultaneously demonstrate satisfactory ductility. It is determined, that the optimal design for these SFRP stirrups involves utilizing narrow and thin stirrups placed at relatively larger intervals. Furthermore, this research delves into assessing the environmental impact of incorporating SFRP stirrups. This assessment enables us to comprehensively evaluate the environmental implications of the entire life cycle of these stirrups in structural beam. Moreover, the analysis reveals that, SFRP stirrups yields lower environmental impacts compared to their steel counterparts, they still provide valuable insights into the overall sustainability considerations within the context of reinforced concrete structures.

Decarbonizing the Transport of Microalgae-based Products - The Role of E-mobility.

BACKGROUND: The decarbonization of road transport is a precondition for achieving carbon neutrality. Battery-electric vehicle technology can make this a reality. In this bias, the objective of the article is to shed light on the ongoing debate about the potentially important role of the adoption of electric vehicles in the transport of microalgae- based products to help them advance to a cleaner life cycle. METHODS: Five routes, including unimodal and multimodal conditions, were defined to assess the carbon emissions of the transport system and, more specifically, of road transport. The headquarters of market-leading microalgae manufacturers were selected as the origin of the routes and, as the destination, regions that sustain them. RESULTS: The results reveal the supremacy of road transport of microalgae-based products using electric vehicles powered by nuclear, hydroelectric, and wind, followed by biomass and photovoltaic energy. They also show that the positive impact of wind, water, and photovoltaic energy on the climate, added to the lower battery charging costs and the greater opportunity to generate revenue from the sale of carbon credits, make their tradeoffs. CONCLUSION: The exquisite results of this study convey key messages to decision-makers and stakeholders about the role of electromobility in building a zero-carbon delivery route.

Consideration of Climate Change on Environmental Impact Assessment in Tanzania: Challenges and Prospects.

The potential of the environmental impact assessment (EIA) process to respond to climate change impacts of development projects can only be realized with the support of policies, regulations, and actors' engagement. While considering climate change in EIA has become partly mandatory through the EU revised Directive in Europe, African countries are still lagging. This paper assesses Tanzanian policies, laws, regulations, and EIA reports to uncover consideration of climate change impacts, adaptation, and mitigation measures, drawing from the transformational role of EIA. The methodology integrates content analysis, interpretive policy analysis, and discourse analysis. The analyses draw from environmental policy, three regulatory documents and three EIA reports in Tanzania using a multi-cases study design. The aim was to understand how considering Climate Change issues in EIA has played out in practice. Results reveal less consideration of climate change issues in EIA. The policy, laws, and regulations do not guide when and how the EIA process should consider climate change-related impacts mitigation and adaptation. The practice of EIA in the country is utterly procedural in line with regulations provisions. Consequently, environmental impact statements only profile the climatology of the study area without conducting a deeper analysis of the historical and future climate to enhance the resilience of proposed projects. The weakness exposed in the laws and regulations contributes to the challenges of responding to the impacts of climate change through the EIA process. It is possible to address climate change issues throughout the project life cycle, including design, approval, implementation, monitoring, and auditing, provided the policy and regulations guide how and when the EIA process should consider climate change issues. Additionally, increasing stakeholders' awareness and participation can enhance the EIA process's potential to respond to the impacts of climate change.

Biological wastewater treatment: a comprehensive sustainability analysis using life cycle assessment.

The research conducts a life cycle assessment (LCA) on wastewater treatment (WWT) methods-membrane bioreactor (MBR), soil biotechnology (SBT), and bio-electrochemical constructed wetlands (BCW)-in comparison with the conventional activated sludge process (ASP). Employing SimaPro v9.5 with a cradle-to-gate system boundary, the analysis utilizes the IMPACT 2002 + method, employing per cubic meter of treated wastewater as the functional unit. The analysis shows that SBT exhibits the lowest environmental impacts among the considered WWT methods. The global warming potential was 0.0996 kg CO2 eq. for SBT, 1.33 kg CO2 eq. for MBR, 0.131 kg CO2 eq. for BCW, and 0.544 kg CO2 eq. for ASP. BCW demonstrates a 75.91% decrease, while MBR exhibits a 144.48% increase compared to ASP. Notably, electricity consumption emerges as the primary contributor to environmental impact in MBR and ASP. The resource impact category varies with a 138.15% increase in MBR and an 83.41% decrease in SBT compared to ASP. Additionally, the research indicates that the high human health impact observed in MBR results mainly from increased carcinogens (0.00176 kg C2H3Cl eq.), non-carcinogens (0.01 kg C2H3Cl eq.), and ionizing radiation (3.34 Bq C-14 eq.). The findings underscore the importance of considering treatment efficiency and broader environmental implications in selecting WWT methods. As the world emphasizes sustainability, such LCA studies provide valuable insights for making informed decisions in wastewater management.

Land Resources for Wind Energy Development Requires Regionalized Characterizations.

Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. We developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m2/MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m2 and 0.05 ± 0.01 m2/MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.

The Global Environmental Impacts of China's Accession to the WTO: A 20-Year Review.

Robust empirical assessments of the long-term cumulative global effects of free trade and economic globalization on the environment are limited. This account fills this gap by constructing a dynamic computable general equilibrium model to estimate the environmental effects of a milestone in the recent history of trade liberalization: China's 20-year World Trade Organization (WTO) accession. The modeling shows that China's accession could have resulted in an increase in the global cumulative greenhouse gases (GHGs), sulfur dioxide (SO2), and nitrogen oxide (NOx) emissions by roughly 14,000 Mt CO2-eq, 64 Mt, and 46 Mt, respectively. The global production scale effect contributed to most of these estimated increases. The regional total output composition effect also caused higher emissions. Meanwhile, the sectoral output composition effect helped reduce total emissions to a limited extent. Fortunately, a package of emission abatement measures led to a decrease in emission factors and a drop in the global cumulative emissions of GHGs, SO2, and NOx. The findings suggest that to enjoy the free trade and economic globalization benefits and minimize the induced emission increases, it is vitally important to systemically reduce emissions across the entire economy and nurture a low-carbon trade regime.

Assessing petrochemical effluent effect on heavy metal pollution in Musa Estuary: A numerical modeling approach.

The objective of this study is to assess the effect of petrochemical effluent on heavy metal pollutant in the Musa Estuary ecosystem in the North-western region of the Persian Gulf, through numerical modeling. The outfall of 30 petrochemical plants poses a potential threat to the estuary's seawater and sediment quality, environment, and public health. A combined hydrodynamic and ecologic modeling framework is applied to predict the spatial distribution of BOD and hazardous heavy metals in this estuary. MIKE 21 Flow Model (FM) CFD software is applied to simulate the tidal waves hydrodynamics, next to applying the MIKE ECO Lab models to predict the distribution of BOD and heavy metals in ambient water. The accuracy of the modeling framework is validated against measured water level, current speed, and water quality data. The results reveal that the level of lead concentration corresponds with the national standard, while the BOD, arsenic, molybdenum and vanadium exceed the limit in some areas, particularly in the tidal zone. The optimal outlet locations that effectively meet the standard concentrations of the heavy metals in the ambient water of the estuary are determined. The results confirm that the new outlet configuration corresponds with the standards: 0.198 µg/L for arsenic concentrations, 0.182 µg/L for molybdenum, 1.530 µg/L for vanadium, and 1.132 mg/L for BOD, at maximum. This study contributes to the perception of estuarine dynamics and provides practical implications for estuarine sustainable management and pollution control.

Implementation of a GEOAI model to assess the impact of agricultural land on the spatial distribution of PM2.5 concentration.

Air pollution is considered one of the major environmental risks to health worldwide. Researchers are making significant efforts to study it, thanks to state-of-art technologies in data collection and processing, and to mitigate its effect. In this context, while a lot is known about the role of urbanization, industries, and transport, the impact of agricultural activities on the spatial distribution of pollution is less studied, despite knowledge about emissions suggest it is not a secondary factor. Therefore, the aim of this study was to assess this impact, and to compare it with that of traditional polluting sources, harvesting the capabilities of GEOAI (Geomatics and Earth Observation Artificial Intelligence). The analysis targeted the highly polluted territory of Lombardy, Italy, considering fine particulate matter (PM2.5). PM2.5 data were obtained from the Copernicus-Atmosphere-Monitoring-Service and processed to infer time-invariant spatial parameters (frequency, intensity and exposure) of concentration across the whole period. An ensemble architecture was implemented, with three blocks: correlation-based features selection, Multiscale-Geographically-Weighted-Regression for spatial enhancement, and a final random forest classifier. Finally, the SHapley Additive exPlanation algorithm was applied to compute the relevance of the different land-use classes on the model. The impact of land-use classes was found significantly higher compared to other published models, showing that the insignificant correlations found in the literature are probably due to an unfit experimental setup. The impact of agricultural activities on the spatial distribution of PM2.5 concentration was comparable to the other considered sources, even when focusing only on the most densely inhabited urban areas. In particular, the agriculture's contribution resulted in pollution spikes rather than in a baseline increase. These results allow to state that public policymakers should consider also agricultural activities for evidence-based decision-making about pollution mitigation.

Analyzing the effectiveness of environmental impact assessment in Paraná, Brazil and California, United States with fuzzy-set qualitative comparative analysis and the analytical hierarchy process.

Environmental impact assessment (EIA) has become one of the most widespread environmental management instruments. Despite this, EIA is routinely criticized for being ineffective at impacting decision-making. This study compared the EIA systems of Paraná, Brazil and California, United States using the effectiveness dimensions from the EIA literature. This study formats the cases into contextual conditions using the fuzzy-set qualitative comparative analysis (fsQCA) to identify the necessary or sufficient conditions that cause effective outcomes. These effectiveness outcomes are then ranked by EIA stakeholders via the analytical hierarchy process (AHP) to identify stakeholder priorities and to improve stakeholder management. The results show that in Paraná stakeholders identified normative effectiveness as the most important dimension, while stakeholders in California identified this dimension as the second-most important following substantive effectiveness. Public participation was found to be a necessary condition for both substantive and normative effectiveness to occur. Early project definition was found to be sufficient for substantive effectiveness and necessary for normative effectiveness, for which stakeholder coordination was a sufficient condition. This suggests that in order for EIA to influence decision-making and foster sustainable development, greater care needs to be taken to actively engage stakeholders in public participation, with clear roles and project design communicated early on, and a clear role for regulatory authority to promote stakeholder coordination for acceptable outcomes. These findings suggest that some effectiveness dimensions are caused by similar conditions, which could help focus stakeholder management efforts and point to new avenues for future EIA effectiveness research.

Examination of recent hydroelectric dam projects in Canada for alignment of baseline studies, predictive modeling, and postdevelopment monitoring phases of aquatic environmental impact assessments.

Environmental impact assessment (EIA) has been widely criticized by the aquatic science community for poorly aligned approaches when selecting endpoints and collecting data during the baseline, predictive modeling, and postdevelopment monitoring phases. If these critical phases of the EIA process are not aligned properly, it can be difficult to evaluate the presence of postdevelopment effects. Examples of the misalignment of these phases include baseline studies failing to measure indicators that are monitored postdevelopment; predictive assessments that do not quantitatively predict conditions or potential impacts postdevelopment; and the failure to identify relevant indicators that may detect effects postdevelopment. For aquatic assessments, understanding how to protect critical ecosystem attributes to satisfy regulatory concerns could help to better align aquatic science monitoring activities across EIA phases. In this article we investigate recent Canadian hydroelectric dam EIAs to evaluate how well recent assessment approaches are meeting these necessary conditions of good aquatic EIA practice through the lens of ecosystem services from a fish's perspective. We found that larger facilities generally had baseline studies and modeling that better supported postdevelopment monitoring, but improvements in structure, linkages, and expectations would better align EIA phases in a manner that would improve assessments and environmental protection. Integr Environ Assess Manag 2024;20:616-644. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Roads as conduits for alien plant introduction and dispersal: The amplifying role of road construction in Ambrosia trifida dispersal.

The initial disruption caused by road construction, combined with ongoing vehicular traffic and regular road maintenance, can repeatedly disturb the environment in ways that favor introduced alien plants. We hypothesized that several characteristics of road construction influence the introduction of alien plants and analyzed 444 Environmental Impact Assessment reports for insights into the relationship between the progression of construction and alien plant richness. Additionally, we believed that roads enhance seed dispersal post-construction, and tested this using Ambrosia trifida patches on completed roads. In 41 construction sites, a total of 137 alien plant species were identified, with 120 introduced after the onset of construction. Significant correlations were found between alien plant richness and road characteristics, with wider roads experiencing more newly introduced species, while longer roads had more total alien plants. As construction progressed, the richness of alien plants generally increased, with around 88 % of sites showing this trend. Changes in alien plant composition during construction revealed a transition from perennial to annual dominance. Post-construction, we found that vehicles played a role in Ambrosia trifida seed dispersal, with seeds predominantly dispersing in the direction of traffic. This study provides information on alien plant species that are commonly introduced and rapidly dispersed due to road construction. Overall, we showed that road construction and subsequent vehicle traffic are primary factors in the spread of alien plants, necessitating early management measures during construction to prevent their proliferation.

Contextual attributes associated with public participation in environmental impact assessments in Thailand: Perspectives obtained from authorities and academics.

Context can enhance or hinder public participation (PP) in environmental impact assessments (EIAs). This study aimed to investigate and discuss how PP-related contextual attributes influence the quality of PP in Thai EIA processes. The study adopted the qualitative approach and interviewed 20 key informants with insightful PP-associated experience in Thai EIAs. The results showed that four major groups of contextual attributes are believed to influence PP in Thai EIAs: the legal and political frameworks, the capacities of key actors, environmental awareness and the right to participate in decision-making processes, and cultural context. The greatest strength of PP in Thai EIAs is that PP is mandated by law, followed by increased environmental awareness and the right to participate in the decision-making process. Different key actors such as project owners, consultants, non-governmental organizations, and reviewing agencies encounter difficulties in discharging their prescribed functions, which affects the quality of PP. The authoritarian culture of Thai society also prevents PP in EIAs. The study offers certain recommendations, including public communication about how civic inputs can influence decision-making processes, the employment of social sector specialists to facilitate PP in EIA, and the application of appropriate participation techniques associated with the prevailing culture.